电力系统无功优化的二次变异遗传算法

在自适应遗传算法的基础上引入优良个体池和二次变异操作,提出了用于电力系统无功优化和电压控制的二次变异遗传算法。该方法建立一个与群体规模等大的优良个体池,用于保存个体编码、适应度等详细数据。每计算完一代,将该代的个体与优良个体池中的个体进行生存竞争,因此优良个体池中保留了历代计算的优良个体,下一代的群体从优良个体池中选择。考虑到遗传操作后存在大量相同个体,检出重复个体进行二次变异,产生邻近的个体,避免了重复计算而且增强了算法的局部搜索能力,加快了算法的收敛速度。该方法和自适应遗传方法用IEEE30节点系统为例计算,结果表明:使用二次变异自适应遗传算法优化的网损平均值更低,寻优性能更好,优化的网损值集中在小的区间。

Reactive power optimization using second mutation genetic algorithm

By introducing fine individual pool and second mutation to adaptive genetic algorithm,a second mutation genetic algorithm is presented for the reactive power optimization and voltage control of power system.A fine individual pool with the same size as the group is established to store the detailed data of individual codes,fitness values,etc..The individuals of each generation compete with the individuals in fine individual pool and the finer stays in pool.The next generation is produced from the fine individual pool.After genetic operation,the same individuals are picked out and mutated for the second time to produce adjacent differed individuals,thus avoiding repeated calculation,enhancing local search ability and speeding up the convergence.Both the proposed method and the adaptive genetic algorithm are applied to IEEE 30-bus system for comparison.Results show that,the average transmission loss optimized by the second mutation adaptive genetic algorithm is lower,with better optimization performance and smaller area of optimized transmission loss values.